Determinación De Relaciones Nivel – Caudal Simple O Compleja en Un Río. Caso Del Río Cauca

Ramírez, Carlos A.; Carvajal, Yesid; Bocanegra, Ricardo A.; Sandoval, Maria C.

doi:10.25100/iyc.v20i2.6354

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…The results follow research by Ramirez et al [3] examining the relationship between water level and flooding. This study uses a logarithmic method and a storage method that provides an opportunity to make adjustments to flood discharge.…”

Section: Discussionsupporting

confidence: 86%

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Biantoro¹,

Wahyudi²,

Ni’am³

2023

cea

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Floods are natural disasters which cause losses that often occur in coastal cities such as Jakarta, Indonesia. Paying attention to rainfall and water flow is in the form of predicting floods. The research goal is to determine the best concentration time method, predict water level and flood inundation, and reduce the impact of flooding for people in the downstream area of the river, Jakarta. The method used in this research is quantitative descriptive analysis, rational approach, time of concentration, artificial neural network (ANN), and the use of the HEC RAS application. The results show that the arrival time of the flood using the Kirpich method shows a concentration time of 12 hours 5 minutes, Travel Time of 12 hours 10 minutes, Velocity of 11 hours 21 minutes and Nakayasu of 9 hours 1 minute. Modelling using an artificial neural network shows that the modelling results can function to predict water levels in the future. The results of the artificial neural network that estimates the water level for the 2020 period are 5.1 m (January), 4.94 m (February), 3.89 m (March), 3.52 m (April), 3.71 m (May), 2.58 m (June), 2.59 m (July), 24.1 m (August), 2.97 m (September), 3.32 m (October), 3.07 m (November) and 3.68 m (December).

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Section: Discussionsupporting

confidence: 86%

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Biantoro¹,

Wahyudi²,

Ni’am³

2023

cea

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“…Estimar caudales con curvas de Nivel-Caudal, implica muchas incertidumbres, que deben considerarse en la hidrometría. Las curvas, pueden ser simples o complejas, según el régimen de flujo y las características del cauce; Ramirez et al, (2009), determinaron estas relaciones en el río Cauca, y el efecto que la operación del embalse Salvajina impone en el río, encontrando que existe una relación compleja entre niveles y caudales, presentando un bucle o "loop", que indica que durante el ascenso de una creciente, se presentan mayores caudales y menores niveles de agua, comparados con el descenso. La aplicación para una creciente moderada, presentó hasta del 19% entre los caudales estimados.En un río de régimen no estacionario (p.e.…”

Section: Aspectos Hidrologicos Y De Toma De Los Datosunclassified

Dimensiones para usar metodologías en la estimación de caudales ambientales en Colombia

Escobar

2016

Respuestas

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La sociedad ha usado el agua superficial y subterránea de diferentes formas (agricultura, consumo urbano, actividades industriales, etc), convirtiendo el recurso en el motor de desarrollo económico de las naciones. No obstante, se ha subestimado el valor de los ecosistemas que mantienen el medio acuático. Los beneficios a corto plazo del uso del agua son muchos, pero serían mayores, si se manejaran de forma sostenible, porque garantizan la provisión de bienes y servicios, la protección de los ecosistemas asociados, así como la capacidad adaptativa ante alteraciones ambientales, como el cambio climático. Determinar caudales ambientales (CA) forma parte de una gestión sostenible del sistema hídrico centrada en el mantenimiento de un buen estado ecológico. Se revisaron diferentes metodologías para estimar CA a nivel mundial y hace una reflexión crítica desde el contexto colombiano de las facilidades y dificultades para implementarlas.Palabras clave: régimen de caudal ambiental, variabilidad intranual e internanual, ecosistemas, hábitat, hidráulica, hidrología, biota acuática.

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Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves

Niazkar

Zakwan

2021

Complexity

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Estimation of discharge flowing through rivers is an important aspect of water resource planning and management. The most common way to address this concern is to develop stage-discharge relationships at various river sections. Various computational techniques have been applied to develop discharge ratings and improve the accuracy of estimated discharges. In this regard, the present study explores the application of the novel hybrid multigene genetic programming-generalized reduced gradient (MGGP-GRG) technique for estimating river discharges for steady as well as unsteady flows. It also compares the MGGP-GRG performance with those of the commonly used optimization techniques. As a result, the rating curves of eight different rivers were developed using the conventional method, evolutionary algorithm (EA), the modified honey bee mating optimization (MHBMO) algorithm, artificial neural network (ANN), MGGP, and the hybrid MGGP-GRG technique. The comparison was conducted on the basis of several widely used performance evaluation criteria. It was observed that no model outperformed others for all datasets and metrics considered, which demonstrates that the best method may be different from one case to another one. Nevertheless, the ranking analysis indicates that the hybrid MGGP-GRG model overall performs the best in developing stage-discharge relationships for both single-value and loop rating curves. For instance, the hybrid MGGP-GRG technique improved sum of square of errors obtained by the conventional method between 4.5% and 99% for six out of eight datasets. Furthermore, EA, the MHBMO algorithm, and artificial intelligence (AI) models (ANN and MGGP) performed satisfactorily in some of the cases, while the idea of combining MGGP with GRG reveals that this hybrid method improved the performance of MGGP in this specific application. Unlike the black box nature of ANN, MGGP offers explicit equations for stream rating curves, which may be counted as one of the advantages of this AI model.

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Determinación De Relaciones Nivel – Caudal Simple O Compleja en Un Río. Caso Del Río Cauca

Cited by 3 publications

References 16 publications

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Analysis of Time of Concentration Estimates Using Some Methods and HEC RAS

Dimensiones para usar metodologías en la estimación de caudales ambientales en Colombia

Assessment of Artificial Intelligence Models for Developing Single-Value and Loop Rating Curves

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